1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
17/10/2022 Comida 47140 Andrés NA
19/10/2022 Comida 28110 Andrés FREST verduras y frutas
23/10/2022 Comida 76701 Tami NA
26/10/2022 Comida 35941 Tami NA
26/10/2022 Enceres 11980 Andrés Mascarilla
27/10/2022 Comida 17536 Tami NA
30/10/2022 VTR 21990 Andrés entel
28/10/2022 Comida 27940 Andrés tres toques
3/11/2022 Diosi 56000 Tami Vacunas
4/11/2022 Electricidad 49266 Andrés Pac enel
6/11/2022 Comida 19325 Tami NA
8/11/2022 Agua 10092 Andrés NA
9/11/2022 Diosi 117980 Andrés 58990 por 2
9/11/2022 Comida 73462 Tami NA
9/11/2022 Diosi 17535 Tami Correa petsu
12/11/2022 Gas 76350 Andrés NA
12/11/2022 Enceres 16986 Andrés uber ida matri fran
14/11/2022 Comida 51263 Tami NA
19/11/2022 Comida 2943 Tami NA
20/11/2022 Transferencia 60000 Tami Deposito 30 lks
22/11/2022 VTR 21990 Andrés entel
22/11/2022 Comida 106204 Tami NA
26/11/2022 Comida 66000 Andrés NA
29/11/2022 Netflix 8240 Tami NA
2/12/2022 Comida 52227 Tami NA
3/12/2022 Electricidad 24773 Andrés es del mes pasado
4/12/2022 Comida 30844 Tami Uber Eats cumpleaños
4/12/2022 Comida 7190 Tami Queso cabra laminado
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 5.1082e+08   2    5.4458 0.0046 ** 
## lag_depvar    7.9428e+10   1 1693.5498 <2e-16 ***
## Residuals     2.4482e+10 522                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr     p adj
## 1-0  7228.838   933.1964 13524.48 0.0196339
## 2-0 27596.020 21811.9903 33380.05 0.0000000
## 2-1 20367.181 16886.8057 23847.56 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
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## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   370 49830.28 15830.262
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2024.350430   4042.713449   -540.361181   2436.086940  -2974.158734 
##             7             8             9            10            11 
##    517.100612  -5658.267623  -1184.966096  -3962.996698   -411.860493 
##            12            13            14            15            16 
##  -4934.467479  -1600.497672   -890.881118    385.634001  -3236.571774 
##            17            18            19            20            21 
##   -369.702231  -2123.029763   6611.926428  -1529.467198  -1207.564264 
##            22            23            24            25            26 
##   1476.911486  -1187.437279    234.563737   1694.201629  -7104.887382 
##            27            28            29            30            31 
##    951.620756   8194.652565    411.844226    -20.323740  -2406.473192 
##            32            33            34            35            36 
##   1573.019314   4567.813004   1118.002066   2382.209016  -1878.746055 
##            37            38            39            40            41 
##   4600.010311   4299.087892  -2283.696987  -2986.202796  -1111.414428 
##            42            43            44            45            46 
## -10741.501378   7299.765745   2559.166658   1365.971987   8103.029487 
##            47            48            49            50            51 
##    676.340914   6519.497943   6699.909116  -5901.561631  -4806.494122 
##            52            53            54            55            56 
##  -5064.707530  -7927.980237   6138.062265  -4075.441415  -4889.356114 
##            57            58            59            60            61 
##   3864.891409    891.934460    -29.458848    144.524358  -4994.614174 
##            62            63            64            65            66 
##  18133.215577   3628.499653  -3660.304011   5916.553636   7330.595351 
##            67            68            69            70            71 
##  14620.035289   1662.533391 -13241.046257  -1317.778598   4634.726896 
##            72            73            74            75            76 
##  -4912.450110  -4410.248588 -10497.977995   2477.044065  -5393.117030 
##            77            78            79            80            81 
##   1075.133514  -6857.268474    561.810428  -2341.997410  -2680.389689 
##            82            83            84            85            86 
##  -3917.264965   -520.655895   2329.989862   3773.796413    482.751901 
##            87            88            89            90            91 
##   -479.994586    201.010005   4305.516309  -1164.314177   1150.845705 
##            92            93            94            95            96 
##  -2065.684751  -1043.290794    179.490586    276.236877  -7483.081191 
##            97            98            99           100           101 
##   2400.290967  -8597.440138  -2927.150738  -4024.468292  -1719.185839 
##           102           103           104           105           106 
##  -1243.952128   3197.672475  -2330.647651   2606.469477  -1150.188237 
##           107           108           109           110           111 
##    978.862415   2593.438589  -3151.532237  -4717.278166   -840.271665 
##           112           113           114           115           116 
##   1913.189712  11700.051487  -1249.039301   2664.181849   4256.305563 
##           117           118           119           120           121 
##   3492.607095  -1112.300978  -4725.922614  -3727.518131   2320.719592 
##           122           123           124           125           126 
##  -1734.147278   1340.982752   8857.414382    837.276622    121.050140 
##           127           128           129           130           131 
##  -2529.551058   2650.496997   7045.842501    999.371139  -8511.821103 
##           132           133           134           135           136 
##   1747.167620   4131.953847  -3171.316549  -1422.869657   -855.219885 
##           137           138           139           140           141 
##  -3880.173910   1186.960623   -493.243483  -2911.107287   1723.395686 
##           142           143           144           145           146 
##  -1878.267642  -7824.877756   2051.463233  -3471.336454   2113.138773 
##           147           148           149           150           151 
##   -250.170740   1029.625018   -354.653864   1356.576057   1189.005335 
##           152           153           154           155           156 
##   3357.369602  -4864.626216  -1171.923746  -3232.380751   5963.062404 
##           157           158           159           160           161 
##   9745.968029  -3183.405637  -4527.641355   3858.106079    442.958111 
##           162           163           164           165           166 
##   2942.627926  -5668.529446  -6499.863471   4410.201875  17638.646256 
##           167           168           169           170           171 
##   3845.055691   -185.715968  -2231.221974   -884.734446   3812.060388 
##           172           173           174           175           176 
##    -11.585342  -7857.693426   3093.531597   4550.211683    843.578695 
##           177           178           179           180           181 
##   8967.906781  -9043.567614  -3251.350392 -10519.144472 -10999.603036 
##           182           183           184           185           186 
##   1489.340661   9541.700277  -1199.155518   6159.905132   6774.928689 
##           187           188           189           190           191 
##  13365.181881   8613.968937  -3894.416446   2641.477290  10541.334300 
##           192           193           194           195           196 
##  -1488.525861  -2283.839017 -10113.019718  -6175.105409   1433.850032 
##           197           198           199           200           201 
##  -5035.370118  -9588.251882   5608.879033  -2854.332021  -1493.586654 
##           202           203           204           205           206 
##   -583.456820   6714.791883  10086.630898    760.516030   3105.876469 
##           207           208           209           210           211 
##   3273.997311   5955.659030  12995.552829  -5547.705846 -11139.322891 
##           212           213           214           215           216 
##  -5482.674033 -10390.380003  -4856.232614   1754.743487 -12787.576626 
##           217           218           219           220           221 
##  16636.712632   8018.870546   1719.895743  26876.829501  12658.106382 
##           222           223           224           225           226 
##   7445.377911  14129.246395  -3831.821498  -1639.753893   3891.945717 
##           227           228           229           230           231 
##    475.761673   2870.788308   9133.080498   5950.916135  -1785.190057 
##           232           233           234           235           236 
##  -1692.445770   9573.405817 -11373.000652  -7117.456724  -8356.707285 
##           237           238           239           240           241 
##  -9898.116497   3301.036920   1567.343716  -8085.653156  -8762.000727 
##           242           243           244           245           246 
##   9338.158089  -7544.914788   2721.812671 -10075.302662  -3809.404255 
##           247           248           249           250           251 
##   1670.893409   1242.887209 -12082.807636   3898.434061   2303.347766 
##           252           253           254           255           256 
##   4442.993447   2352.413285   -950.693837  11349.177111  21062.750489 
##           257           258           259           260           261 
##   3328.504725  -4143.707867   4251.597378  -1558.522903   3881.585502 
##           262           263           264           265           266 
##  -4712.362774 -10738.582445  -4545.251752   -327.143214  -4993.568980 
##           267           268           269           270           271 
##   8983.625586  -4099.540652   4379.630943  -1929.106286   4613.047267 
##           272           273           274           275           276 
##    878.652581   7470.492746  -1263.793235  12178.253737  -4463.145540 
##           277           278           279           280           281 
##   1861.290526   -238.902982   7988.418337  -4939.632432  -2594.464250 
##           282           283           284           285           286 
## -11113.060597  -2485.518114  18846.204832   7919.428191   2850.034711 
##           287           288           289           290           291 
##   -516.157829   1025.277864   6519.234989   6988.613805 -18681.006211 
##           292           293           294           295           296 
## -10980.430379  -7923.840348   9888.950867   3263.599438   -997.214957 
##           297           298           299           300           301 
##  27588.264915  10162.697863   4972.952208   9584.325021   2903.030605 
##           302           303           304           305           306 
##   -981.925760   7963.606506 -24242.255786  -3384.623043     -6.882223 
##           307           308           309           310           311 
##  -6794.762556  -3770.333401   3149.135430  -8985.166034  -2989.393171 
##           312           313           314           315           316 
##  -7935.199665   1843.460664  -2883.736040   2322.113371  -3822.217558 
##           317           318           319           320           321 
##  27714.715256   -580.452152   3440.097167  10969.860106   5692.938477 
##           322           323           324           325           326 
##  32471.841905   5095.886319 -20947.412105   1881.499077   1204.974373 
##           327           328           329           330           331 
##  -6363.095649  -1597.361820 -33116.534035   1200.223771  -1989.580055 
##           332           333           334           335           336 
##    225.965081  -2851.929602   4409.913432   -134.240026  -6651.702886 
##           337           338           339           340           341 
##  -2791.612798  -1859.882304  -7345.305432   4209.869215  -1038.473712 
##           342           343           344           345           346 
##  -1406.994537   -663.250567    504.089107    801.256912  -1307.693650 
##           347           348           349           350           351 
##  -9135.560760 -12867.287153   2697.013526  -3959.360472  -3287.980702 
##           352           353           354           355           356 
##  -5606.002540   2137.201864   1749.151826   3099.056710  -3444.265069 
##           357           358           359           360           361 
##   -187.883916    997.972914   7322.080568    546.226635    225.685058 
##           362           363           364           365           366 
##   2843.436440  -2504.217604   -619.787671  -8482.943418  -4326.296333 
##           367           368           369           370           371 
##  -5895.365557  -4609.550947  -6897.816701   5393.065273    712.405883 
##           372           373           374           375           376 
##   7449.076569  -7349.097886  -1944.688948  -3065.469213  -2135.470419 
##           377           378           379           380           381 
## -12121.842079   2287.692311 -10271.354722   6096.384987   9693.754082 
##           382           383           384           385           386 
##   3426.976285  -2120.314277   1888.868108   7014.805909  11644.021540 
##           387           388           389           390           391 
##  -5626.912171  -5160.732388     68.678605   8788.235842   1996.861182 
##           392           393           394           395           396 
##  11396.278887  -9756.680069   2947.530187    874.746990    724.433928 
##           397           398           399           400           401 
##   -490.803874   -393.171497 -14311.185023   8778.450626   -964.255739 
##           402           403           404           405           406 
##  -1146.618185   7216.691650  -7731.273673  -1051.216932  -2276.915076 
##           407           408           409           410           411 
##  -5549.669317  -2559.035930  -3604.429596  -8426.267896   6500.967133 
##           412           413           414           415           416 
##   1966.415360  -7063.296330  -7350.508519  14593.302232   4100.841499 
##           417           418           419           420           421 
##   4748.441620  -7807.853432  -4474.738761  -2308.254389   3123.466337 
##           422           423           424           425           426 
## -13725.945346  -2437.700700  -8738.929086   3410.032650   7345.096641 
##           427           428           429           430           431 
##   6895.003541  -3710.335760  -3826.645057  -4410.934286  -1459.499913 
##           432           433           434           435           436 
##  -5379.663339  -6272.900548  -5571.914483   -997.512581   -458.829793 
##           437           438           439           440           441 
##  -4595.275305   2973.821920   5204.342076  -4728.373527  -1812.417377 
##           442           443           444           445           446 
##   1923.742825  -3507.204127   3177.049861  -6259.751961 -11765.644176 
##           447           448           449           450           451 
##  -4115.275982  10050.295877  -1688.311379   5100.058610  -5555.366106 
##           452           453           454           455           456 
##   -784.633151    720.324028   3353.943558 -11961.729597   3730.711283 
##           457           458           459           460           461 
##  -6365.325232   6883.332846   3332.564508   2807.212041  -3561.305890 
##           462           463           464           465           466 
##   2393.806774    280.201662   2078.363735   -246.457742   3626.699838 
##           467           468           469           470           471 
##  -2380.080066   6078.249881  -6696.570919  -2681.310801  -1907.196715 
##           472           473           474           475           476 
##  -4355.635374   3325.118652   8107.309617  -5747.317450   1784.014493 
##           477           478           479           480           481 
##  -5887.511471  -2524.184386   2342.410278 -12613.390012  -9382.301626 
##           482           483           484           485           486 
##   -794.111024    420.875709   -574.163933   -960.675557  -9207.997880 
##           487           488           489           490           491 
##  11505.403728   6580.780181   7729.791085  -5165.113151   5664.271131 
##           492           493           494           495           496 
##   9562.910203   6280.975185 -13269.912966 -10291.869783  -3116.478050 
##           497           498           499           500           501 
##   -768.466370   -186.442629  -7290.368518    977.527992   4644.112149 
##           502           503           504           505           506 
##   5838.873111    961.096854    376.923832  -6943.805830    897.785598 
##           507           508           509           510           511 
##  -4726.646476   2174.218954   -968.113448  -7827.442803   -238.866685 
##           512           513           514           515           516 
##  -2315.944329   -224.131780   1690.857321  -9149.728457  -7383.188418 
##           517           518           519           520           521 
##  24693.282477  10109.837847   6118.696199  -5119.328249   3045.007364 
##           522           523           524           525           526 
##  17256.218056  11636.949359 -24026.981521  -4812.178858  -3458.783257 
##           527 
##   4864.858647 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17244.94 20096.29 24356.50 24074.06 26430.87 23759.61 24476.98 19702.11 
##       10       11       12       13       14       15       16       17 
## 19438.28 16777.15 17555.75 14280.35 14331.60 14997.22 16696.29 15013.85 
##       18       19       20       21       22       23       24       25 
## 16050.03 15422.65 22515.47 21598.14 21077.23 22970.01 22295.01 22948.51 
##       26       27       28       29       30       31       32       33 
## 24797.17 18716.66 20445.35 28294.16 28351.90 28024.33 25650.27 27054.76 
##       34       35       36       37       38       39       40       41 
## 30903.43 31252.36 32663.60 30170.56 34143.91 37356.70 34408.49 31214.70 
##       42       43       44       45       46       47       48       49 
## 30060.79 20626.52 28156.26 30596.31 31687.11 38535.23 38029.07 42698.09 
##       50       51       52       53       54       55       56       57 
## 46940.56 39627.78 34188.28 29203.69 22338.08 28637.30 25212.93 21505.11 
##       58       59       60       61       62       63       64       65 
## 25919.92 27181.32 27478.76 27891.19 23756.07 40371.64 42218.30 37457.30 
##       66       67       68       69       70       71       72       73 
## 41670.40 46593.25 57277.04 55287.90 40509.49 38011.70 41034.02 35325.82 
##       74       75       76       77       78       79       80       81 
## 30771.41 21461.24 24667.40 20587.15 22676.27 17564.33 19582.71 18808.10 
##       82       83       84       85       86       87       88       89 
## 17834.41 15900.51 17180.15 20793.49 25217.68 26208.99 26233.99 26851.63 
##       90       91       92       93       94       95       96       97 
## 30982.74 29811.58 30812.40 28874.01 28072.65 28441.33 28848.51 22416.57 
##       98       99      100      101      102      103      104      105 
## 25436.01 18456.29 17310.75 15348.61 15648.81 16327.18 20806.36 19888.53 
##      106      107      108      109      110      111      112      113 
## 23404.76 23194.42 24872.99 27753.96 25248.42 21686.70 21962.52 24612.66 
##      114      115      116      117      118      119      120      121 
## 35493.04 33683.25 35523.41 38526.11 40484.87 38169.92 32983.38 29319.42 
##      122      123      124      125      126      127      128      129 
## 31405.29 29682.73 30866.01 38476.87 38118.81 37178.98 34037.93 35821.73 
##      130      131      132      133      134      135      136      137 
## 41227.49 40666.96 31855.83 33122.47 36316.89 32722.30 31107.22 30190.89 
##      138      139      140      141      142      143      144      145 
## 26742.90 28159.39 27928.68 25611.60 27638.98 26261.73 19854.54 22889.48 
##      146      147      148      149      150      151      152      153 
## 20713.00 23694.46 24235.23 25827.94 26010.28 27666.85 28969.49 32006.05 
##      154      155      156      157      158      159      160      161 
## 27469.64 26731.52 24283.22 30185.89 41203.83 39531.64 36892.75 41920.33 
##      162      163      164      165      166      167      168      169 
## 43330.94 46751.82 42211.15 37511.51 42944.64 59270.52 61485.86 59897.65 
##      170      171      172      173      174      175      176      177 
## 56718.73 55115.65 57822.16 56844.84 49125.75 51953.36 55701.42 55737.66 
##      178      179      180      181      182      183      184      185 
## 62876.85 53365.35 50111.57 40906.89 32433.95 35947.30 46065.44 45520.67 
##      186      187      188      189      190      191      192      193 
## 51482.07 57235.39 68034.03 73324.56 67010.09 67203.81 74284.38 69954.55 
##      194      195      196      197      198      199      200      201 
## 65470.88 54699.11 48720.58 50146.94 45735.25 37892.69 44326.76 42551.59 
##      202      203      204      205      206      207      208      209 
## 42189.03 42668.07 49471.94 58374.06 58003.12 59730.43 61388.63 65185.30 
##      210      211      212      213      214      215      216      217 
## 74665.56 66736.89 54908.82 49509.81 40493.09 37446.40 40564.58 30570.29 
##      218      219      220      221      222      223      224      225 
## 47568.42 54899.82 55803.03 78601.47 86107.34 88113.47 95715.82 86653.61 
##      226      227      228      229      230      231      232      233 
## 80643.34 80224.67 76869.78 76030.06 80773.94 82140.19 76567.59 71773.59 
##      234      235      236      237      238      239      240      241 
## 77435.43 64063.89 56088.85 48027.83 39627.25 43825.23 45981.08 39422.29 
##      242      243      244      245      246      247      248      249 
## 33092.70 43390.06 37628.62 41570.02 33822.69 32526.68 36187.26 39015.24 
##      250      251      252      253      254      255      256      257 
## 29831.42 35778.08 39585.01 44787.30 47509.55 47001.39 57317.25 74839.78 
##      258      259      260      261      262      263      264      265 
## 74654.57 67955.55 69439.52 65654.84 67103.08 60851.73 50110.82 46132.43 
##      266      267      268      269      270      271      272      273 
## 46342.14 42443.23 51260.11 47527.80 51680.53 49794.38 53867.63 54164.08 
##      274      275      276      277      278      279      280      281 
## 60190.22 57821.03 67508.00 61424.00 61634.33 59981.01 65732.20 59453.61 
##      282      283      284      285      286      287      288      289 
## 56012.49 45549.66 43944.08 61201.29 66739.39 67149.44 64563.29 63649.34 
##      290      291      292      293      294      295      296      297 
## 67656.10 71572.01 52541.00 42628.70 36631.05 46967.40 50213.93 49326.59 
##      298      299      300      301      302      303      304      305 
## 73558.02 79512.05 80180.67 84799.83 82995.78 78018.82 81490.68 56353.05 
##      306      307      308      309      310      311      312      313 
## 52608.74 52288.05 46069.19 43274.58 46883.17 39424.54 38144.77 32698.40 
##      314      315      316      317      318      319      320      321 
## 36488.45 35668.60 39505.65 37487.14 63311.02 61149.05 62775.00 70784.78 
##      322      323      324      325      326      327      328      329 
## 73175.59 98694.40 97069.70 72864.64 71660.74 70015.67 61955.65 59073.68 
##      330      331      332      333      334      335      336      337 
## 28978.20 32671.15 33111.32 35434.64 34774.52 40549.95 41627.13 36867.76 
##      338      339      340      341      342      343      344      345 
## 36081.03 36207.88 31519.99 37527.76 38192.14 38450.96 39328.05 41116.60 
##      346      347      348      349      350      351      352      353 
## 42941.27 42692.56 35626.86 26180.84 31533.36 30392.69 29982.15 27595.08 
##      354      355      356      357      358      359      360      361 
## 32280.85 36040.66 40510.84 38697.17 39959.31 42100.92 49507.06 50058.46 
##      362      363      364      365      366      367      368      369 
## 50260.42 52727.22 50206.93 49650.66 42285.01 39477.65 35648.98 33424.39 
##      370      371      372      373      374      375      376      377 
## 29476.36 36775.02 39065.35 46962.53 40925.26 40371.61 38906.76 38438.84 
##      378      379      380      381      382      383      384      385 
## 29293.02 33897.93 26939.33 35170.82 45519.17 49089.89 47360.70 49355.34 
##      386      387      388      389      390      391      392      393 
## 55584.69 65084.20 58285.45 52745.46 52473.76 59864.28 60388.44 69069.97 
##      394      395      396      397      398      399      400      401 
## 58159.47 59728.68 59288.14 58771.23 57255.89 56015.61 42754.55 51352.97 
##      402      403      404      405      406      407      408      409 
## 50351.90 49316.59 55727.42 48258.79 47568.92 45893.10 41563.89 40392.86 
##      410      411      412      413      414      415      416      417 
## 38453.84 32539.18 40423.73 43354.44 38018.79 33099.70 47993.59 51844.13 
##      418      419      420      421      422      423      424      425 
## 55779.28 48237.17 44554.97 43228.96 46820.80 35222.56 34951.36 29201.54 
##      426      427      428      429      430      431      432      433 
## 34799.76 43139.85 50042.34 46802.93 43867.22 40787.79 40675.81 37148.33 
##      434      435      436      437      438      439      440      441 
## 33280.91 30510.80 32089.26 33941.42 31943.04 36816.52 43031.37 39778.85 
##      442      443      444      445      446      447      448      449 
## 39484.40 42495.35 40378.24 44373.75 39613.50 30632.28 29467.99 40842.03 
##      450      451      452      453      454      455      456      457 
## 40523.08 46182.79 41812.35 42162.53 43785.49 47509.30 37368.29 42224.90 
##      458      459      460      461      462      463      464      465 
## 37641.24 45221.72 48747.07 51371.59 48096.19 50440.51 50642.35 52392.03 
##      466      467      468      469      470      471      472      473 
## 51888.87 54837.08 52161.32 57220.14 50469.88 48077.20 46661.21 43280.45 
##      474      475      476      477      478      479      480      481 
## 47042.26 54516.89 48935.41 50641.23 45422.18 43798.73 46635.96 36034.16 
##      482      483      484      485      486      487      488      489 
## 29585.97 31458.12 34158.88 35651.10 36618.43 30249.60 42798.79 49469.07 
##      490      491      492      493      494      495      496      497 
## 56309.68 51013.16 55853.52 63498.74 67315.91 53551.44 44115.05 42137.04 
##      498      499      500      501      502      503      504      505 
## 42460.73 43253.08 37731.47 40134.03 45443.56 51133.76 51844.50 51955.23 
##      506      507      508      509      510      511      512      513 
## 45647.64 46989.65 43243.21 46002.83 45668.01 39374.30 40507.09 39680.99 
##      514      515      516      517      518      519      520      521 
## 40788.29 43432.30 36261.62 31533.86 55459.59 63632.59 67291.04 60660.14 
##      522      523      524      525      526      527 
## 62001.64 75607.76 82594.98 57507.46 52369.78 49059.14 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8448
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    5.445789  0.5632367    3.082787
## t2* 1693.549759 28.4562561  237.973912
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.742328       5.586857   11.71223
## 2    lag_depvar 1356.744188    1705.571122 2137.53291

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Dec 12 00:56:47 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Dec 12 00:56:57 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Dec 12 00:57:06 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Dec 12 00:57:17 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Dec 12 00:57:27 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Dec 12 00:57:36 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Dec 12 00:57:46 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Dec 12 00:57:55 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Dec 12 00:58:05 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Dec 12 00:58:14 2022
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua NA 5.902182 5.874522 7.267629
Comida NA 310.757182 314.482087 341.379771
Comunicaciones NA 0.000000 0.000000 0.000000
Electricidad NA 44.372273 36.624826 31.132171
Enceres NA 21.912454 18.202217 25.337000
Farmacia NA 1.998182 8.257957 10.239257
Gas/Bencina NA 48.354546 30.213217 25.771543
Diosi NA 30.607091 42.127478 40.411086
donaciones/regalos NA 0.000000 7.481826 7.849114
Electrodomésticos/ Mantención casa NA 4.302545 31.585565 23.699086
VTR NA 25.444546 22.127522 21.094229
Netflix NA 7.045364 7.036696 7.451743
Otros NA 3.437546 1.644043 1.080371
Total 0 504.133909 525.657956 542.713000
## Joining, by = "word"


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: 41 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1835, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2023-01-09 00:04:58 sería de: 36.634 pesos// Percentil 95% más alto proyectado: 40.322,53

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 35356.81 35322.97
Lo.80 35489.41 35478.31
Point.Forecast 36633.56 38579.96
Hi.80 38629.41 43156.93
Hi.95 39729.60 45579.83


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      mean
##       0.2677  997.3571
## s.e.  0.1480   33.5237
## 
## sigma^2 = 29209:  log likelihood = -300.78
## AIC=607.55   AICc=608.13   BIC=613.04
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,1) errors 
## 
## Coefficients:
##          ar1      ma1     xreg
##       0.8569  -0.6300  32.5034
## s.e.  0.1498   0.2168   2.0682
## 
## sigma^2 = 29138:  log likelihood = -300.29
## AIC=608.59   AICc=609.56   BIC=615.9
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 911.1358 649.7000 709.5446
Lo.80 1037.3242 770.0363 794.7540
Point.Forecast 1275.6998 997.3570 984.6260
Hi.80 1514.0754 1224.6778 1281.7644
Hi.95 1640.2637 1345.0141 1473.7990


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 42 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Andrés Tami
1 marzo_2019 68268 175533
2 abril_2019 55031 152640
3 mayo_2019 192219 152985
4 junio_2019 84961 291067
5 julio_2019 205893 241389


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.9          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.11         scales_1.2.1        ggiraph_0.8.5      
##  [7] tidytext_0.3.4      DT_0.26             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.1       xts_0.12.2         
## [13] forecast_8.19       wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.0     tm_0.7-9            NLP_0.2-1          
## [19] tsibble_1.1.3       forcats_0.5.2       dplyr_1.0.10       
## [22] purrr_0.3.5         tidyr_1.2.1         tibble_3.1.8       
## [25] ggplot2_3.4.0       tidyverse_1.3.2     sjPlot_2.8.12      
## [28] lattice_0.20-45     gridExtra_2.3       plotrix_3.8-2      
## [31] sparklyr_1.7.9      httr_1.4.4          readxl_1.4.1       
## [34] zoo_1.8-11          stringr_1.5.0       stringi_1.7.8      
## [37] DataExplorer_0.8.2  data.table_1.14.6   reshape2_1.4.4     
## [40] fUnitRoots_4021.80  plyr_1.8.8          readr_2.1.3        
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2          tidyselect_1.2.0    lme4_1.1-31        
##   [4] htmlwidgets_1.5.4   munsell_0.5.0       codetools_0.2-18   
##   [7] its.analysis_1.6.0  withr_2.5.0         colorspace_2.0-3   
##  [10] ggfortify_0.4.15    highr_0.9           knitr_1.41         
##  [13] uuid_1.1-0          rstudioapi_0.14     TTR_0.24.3         
##  [16] labeling_0.4.2      emmeans_1.8.3       slam_0.1-50        
##  [19] bit64_4.0.5         farver_2.1.1        datawizard_0.6.4   
##  [22] fBasics_4021.93     rprojroot_2.0.3     vctrs_0.5.1        
##  [25] generics_0.1.3      xfun_0.35           timechange_0.1.1   
##  [28] R6_2.5.1            bitops_1.0-7        cachem_1.0.6       
##  [31] assertthat_0.2.1    networkD3_0.4       vroom_1.6.0        
##  [34] nnet_7.3-16         googlesheets4_1.0.1 gtable_0.3.1       
##  [37] spatial_7.3-14      timeDate_4021.107   rlang_1.0.6        
##  [40] forge_0.2.0         systemfonts_1.0.4   splines_4.1.2      
##  [43] lazyeval_0.2.2      gargle_1.2.1        selectr_0.4-2      
##  [46] broom_1.0.1         yaml_2.3.6          abind_1.4-5        
##  [49] modelr_0.1.10       crosstalk_1.2.0     backports_1.4.1    
##  [52] quantmod_0.4.20     tokenizers_0.2.3    tools_4.1.2        
##  [55] ellipsis_0.3.2      gplots_3.1.3        jquerylib_0.1.4    
##  [58] Rcpp_1.0.9          base64enc_0.1-3     fracdiff_1.5-2     
##  [61] haven_2.5.1         fs_1.5.2            magrittr_2.0.3     
##  [64] timeSeries_4021.105 lmtest_0.9-40       reprex_2.0.2       
##  [67] googledrive_2.0.0   mvtnorm_1.1-3       sjmisc_2.8.9       
##  [70] hms_1.1.2           evaluate_0.18       xtable_1.8-4       
##  [73] sjstats_0.18.2      ggeffects_1.1.4     compiler_4.1.2     
##  [76] KernSmooth_2.23-20  crayon_1.5.2        minqa_1.2.5        
##  [79] htmltools_0.5.4     tzdb_0.3.0          lubridate_1.9.0    
##  [82] DBI_1.1.3           sjlabelled_1.2.0    dbplyr_2.2.1       
##  [85] MASS_7.3-54         boot_1.3-28         Matrix_1.5-3       
##  [88] car_3.1-1           cli_3.4.1           quadprog_1.5-8     
##  [91] parallel_4.1.2      insight_0.18.8      igraph_1.3.5       
##  [94] pkgconfig_2.0.3     xml2_1.3.3          bslib_0.4.1        
##  [97] estimability_1.4.1  anytime_0.3.9       snakecase_0.11.0   
## [100] janeaustenr_1.0.0   digest_0.6.30       janitor_2.1.0      
## [103] rmarkdown_2.18      cellranger_1.1.0    curl_4.3.3         
## [106] gtools_3.9.4        urca_1.3-3          nloptr_2.0.3       
## [109] lifecycle_1.0.3     nlme_3.1-153        jsonlite_1.8.4     
## [112] tseries_0.10-52     carData_3.0-5       viridisLite_0.4.1  
## [115] fansi_1.0.3         pillar_1.8.1        fastmap_1.1.0      
## [118] glue_1.6.2          bayestestR_0.13.0   bit_4.0.5          
## [121] sass_0.4.4          performance_0.10.1  r2d3_0.2.6         
## [124] caTools_1.18.2
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))